Parameter-free Dynamic Graph Embedding for Link Prediction

Dynamic interaction graphs have been widely adopted to model the evolution of user-item interactions over time. There are two crucial factors when modelling user preferences for link prediction in dynamic interaction graphs: 1) collaborative relationship among users and 2) user personalized interaction patterns. Existing methods often implicitly consider these two factors together, which may lead to noisy user modelling when the two factors diverge. In addition, they usually require time-consuming parameter learning with back-propagation, which is prohibitive for real-time user preference modelling. To this end, this paper proposes FreeGEM, a parameter-free dynamic graph embedding method for link prediction. Firstly, to take advantage of the collaborative relationships, we propose an incremental graph embedding engine to obtain user/item embeddings, which is an Online-Monitor-Offline architecture consisting of an Online module to approximately embed users/items over time, a Monitor module to estimate the approximation error in real time and an Offline module to calibrate the user/item embeddings when the online approximation errors exceed a threshold. Meanwhile, we integrate attribute information into the model, which enables FreeGEM to better model users belonging to some under represented groups. Secondly, we design a personalized dynamic interaction pattern modeller, which combines dynamic time decay with attention mechanism to model user short-term interests. Experimental results on two link prediction tasks show that FreeGEM can outperform the state-of-the-art methods in accuracy while achieving over 36X improvement in efficiency. All code and datasets can be found in https://github.com/FudanCISL/FreeGEM.Comment: 19 pages, 9 figures, 13 tables, Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS 2022), preprint versio

AutoSeqRec: Autoencoder for Efficient Sequential Recommendation

Sequential recommendation demonstrates the capability to recommend items by modeling the sequential behavior of users. Traditional methods typically treat users as sequences of items, overlooking the collaborative relationships among them. Graph-based methods incorporate collaborative information by utilizing the user-item interaction graph. However, these methods sometimes face challenges in terms of time complexity and computational efficiency. To address these limitations, this paper presents AutoSeqRec, an incremental recommendation model specifically designed for sequential recommendation tasks. AutoSeqRec is based on autoencoders and consists of an encoder and three decoders within the autoencoder architecture. These components consider both the user-item interaction matrix and the rows and columns of the item transition matrix. The reconstruction of the user-item interaction matrix captures user long-term preferences through collaborative filtering. In addition, the rows and columns of the item transition matrix represent the item out-degree and in-degree hopping behavior, which allows for modeling the user's short-term interests. When making incremental recommendations, only the input matrices need to be updated, without the need to update parameters, which makes AutoSeqRec very efficient. Comprehensive evaluations demonstrate that AutoSeqRec outperforms existing methods in terms of accuracy, while showcasing its robustness and efficiency.Comment: 10 pages, accepted by CIKM 202

Enhancing CTR Prediction with Context-Aware Feature Representation Learning

CTR prediction has been widely used in the real world. Many methods model feature interaction to improve their performance. However, most methods only learn a fixed representation for each feature without considering the varying importance of each feature under different contexts, resulting in inferior performance. Recently, several methods tried to learn vector-level weights for feature representations to address the fixed representation issue. However, they only produce linear transformations to refine the fixed feature representations, which are still not flexible enough to capture the varying importance of each feature under different contexts. In this paper, we propose a novel module named Feature Refinement Network (FRNet), which learns context-aware feature representations at bit-level for each feature in different contexts. FRNet consists of two key components: 1) Information Extraction Unit (IEU), which captures contextual information and cross-feature relationships to guide context-aware feature refinement; and 2) Complementary Selection Gate (CSGate), which adaptively integrates the original and complementary feature representations learned in IEU with bit-level weights. Notably, FRNet is orthogonal to existing CTR methods and thus can be applied in many existing methods to boost their performance. Comprehensive experiments are conducted to verify the effectiveness, efficiency, and compatibility of FRNet.Comment: SIGIR 202

Real-time Event Analysis in Online Social Networks

Online social networks(OSNs) enable real-time event discussion. Due to the word-of-mouth effect, popular events are disseminated exponentially in a short period of time. With highly active public engagement, new events are being self-reported and discussed live. Compared to traditional news event detection and tracking, this huge volume of data, unstructured content, and variety of information in OSNs pose both opportunities and challenges for event analysis in new environments. This thesis makes key contributions in the following three aspects. Event context identification helps to answer the question of who is interested in the events. It enables applications like user participation prediction, relevant event recommendation and friendship recommendation. We incorporate anchor information into the traditional probability matrix factorization framework to identify the group of users who are interested in given event. Our evaluation based on one-month of 461 events and 1.1 million users shows that our approach outperforms at least 20% over existing approaches. Location inference addresses the problem of lacking location information in event analysis. It helps to understand where the event is being discussed. We use both textual and structural information to predict locations respectively, and finally use a learn-to-rank algorithm to effectively fuse the results. Evaluation a three-month of 0.82 million users, 16.4 million messages, and 11.5 million friendships shows the performance boost of 25% reduction in average error, and 66% reduction in median error over existing work. Event modeling provides a solution for understanding the structure of the event. We first build a hierarchical and incremental model for each event, and then identify the causal relationships within the event structure. Our evaluation on 3.5 million messages over a 5-month period and demonstrate the high effectiveness and efficiency of our approach

A semantic embedding enhanced topic model for user-generated textual content modeling in social ecosystems

The development of Information and Communication Technologies (ICT) and Web 2.0 promotes the emergence of diverse social ecosystems like social Internet of Things (IoT), social media and online communities. User-generated textual content (UGTC), which consists of unstructured texts, is the most important and common type of user-generated content in social ecosystems. UGTC in social ecosystems is generated according to two types of context information—global context (topics) and local context (semantic regularities). For UGTC modeling, topic models just consider global context but ignore semantic regularities, while semantic embedding models are on the opposite. So only utilizing topic models or semantic embedding models to model UGTC suffers from some drawbacks. For this problem, we propose a semantic embedding enhanced topic model named SEE-Twitter-LDA for accurately modeling UGTC in social ecosystems. The core of SEE-Twitter-LDA is that words are generated according to mutual semantic information of topics and semantic regularities. So global context and local context are jointly considered for UGTC modeling. By utilizing 553 098 tweets sampled from Twitter and 211 233 posts sampled from Weibo, we validate SEE-Twitter-LDA’s better performance on perplexity, topic divergence and topic coherence versus existing related models

Studying and understanding characteristics of post-syncing practice and goal in social network sites

Many popular social network sites (SNSs) provide the post-syncing functionality, which allows users to synchronize posts automatically among different SNSs. Nowadays there exists divergence on this functionality from the view of sink SNS. The key to solving this problem is to understand the characteristics of users’ post-syncing practice and goals and evaluate whether they are consistent with an SNS’s norms, cultures, and goals. However, studying and understanding the characteristics of post-syncing practice and goal are challenging tasks as a result of the difficulty of data sampling and the complexity of post-syncing behavior. In this article, we focus on investigating this question by quantitative analysis in combination with qualitative analysis. In the quantitative study, by utilizing 211,233 synced-posts sampled from Weibo, we aim to investigate characteristics of post-syncing from three perspectives: user, content, and goal. The results suggest that post-syncing plays an important role in exhibiting one’s current activities, creations, and skills as well as advertisements but involves a risk of exhibiting personal sensitive profiles. To understand the results, we present an interview-based qualitative study based on thematic analysis. It indicates that the publicity, urgency, and remarkableness of contents and differences of social affordances and social circles between sink SNS and source SNS as well as the one-time consent of post-syncing authentication jointly account for the major role of post-syncing. Based on these results, we propose insights for post-syncing functionality’s adoption, design, and promotion

Building user-oriented personalized machine translator based on user-generated textual content

Machine Translation (MT) has been a very useful tool to assist multilingual communication and collaboration. In recent years, by taking advantage of the exciting developments of neural networks and deep learning, the accuracy and speed of machine translation have been continuously improved. However, most machine translation methods and systems are data-driven. They tend to select a consensus response represented in training data, while a user's preferred linguistic style, which is important for translation comprehension and user experience, is ignored. For this problem, we aim to build a user-oriented personalized machine translation model in this paper. The model aims to learn each user's linguistic style from the textual content that is generated by her/him (User-Generated Textual Content, UGTC) in social media context and generate personalized translation results utilizing several state-of-the-art deep learning techniques like Transformer and pre-training. We also implemented a user-oriented personalized machine translator using Weibo as a case of the source of UGTC to provide a systematical implementation scheme of a user-oriented personalized machine translation system based on our model. The translator was evaluated by automatic evaluation in combination with human evaluation. The results suggest that our model can generate more personalized, natural and lively translation results and enhance the comprehensibility of translation results, which makes its generations more preferred by users versus general translation results